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1. HOW TO INSTALL PYSPARK WITH PIP FOR FREE
2. HOW TO INSTALL PYSPARK WITH PIP HOW TO
3. HOW TO INSTALL PYSPARK WITH PIP DRIVER
4. HOW TO INSTALL PYSPARK WITH PIP CODE

HOW TO INSTALL PYSPARK WITH PIP DRIVER

First, let’s pull the postgres driver jar into your image. Next, we will use the jar method to install the necessary sql driver so that Spark may write directly to postgres. Now you should be able to import koalas directly into your python code. Copy this file into your Docker image and add the following command RUN pip3 install -r requirements.txt. In your application repo, create a file called requirements.txt, and add the following line – koalas=1.8.1. The standard convention is to create a requirements file that lists all of your python dependencies and use pip to install each library into your environment. To install koalas, we will use pip to add the package directly to our python environment. Both methods achieve the same result, but certain libraries or packages may only be available to install with one method. Alternatively, you can download the library jar into your Docker image and move the jar to /opt/spark/jars.

For language specific libraries, you can use a package manager like pip for python or sbt for scala to directly install the library. There are a few methods to include external libraries in your Spark application. To start, we will build our Docker image using the latest Data Mechanics base image – gcr.io/datamechanics/spark:platform-3.1-latest. If you do not have a postgres database handy, you can create one locally using the following steps:ĭocker run -e POSTGRES_PASSWORD=postgres -e POSTGRES_USER=postgres -d -p 5432:5432 postgres ‍ Building the Docker Image The bucket is public, but AWS requires user creation in order to read from public buckets). In this tutorial we use Spark 3.1, but in the future you won’t need to install Koalas, it will work out of the box.įinding the median values using Spark can be quite tedious, so we will utilize the koalas functionality for a concise solution (Note: To read from the public data source, you will need access to an AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. To find the median, we will utilize koalas, a Spark implementation of the pandas API.įrom Spark 3.2+, the pandas library will be automatically bundled with open-source Spark. ‍For the purpose of this demonstration, we will create a simple PySpark application that reads population density data per country from the public dataset –, applies transformations to find the median population, and writes the results to a postgres instance. In this tutorial, we’ll walk you through the process of building a new Docker image from one of our base images, adding new dependencies, and testing the functionality you have installed by using the Koalas library and writing some data to a postgres Database.

HOW TO INSTALL PYSPARK WITH PIP FOR FREE

You can read more about these images here, and download them for free on Dockerhub.

HOW TO INSTALL PYSPARK WITH PIP CODE

Docker containers are also a great way to develop and test Spark code locally, before running it at scale in production on your cluster (for example a Kubernetes cluster).Īt Data Mechanics we maintain a fleet of Docker images which come built-in with a series of useful libraries like the data connectors to data lakes, data warehouses, streaming data sources, and more.

Using Docker means that you can catch this failure locally at development time, fix it, and then publish your image with the confidence that the jars and the environment will be the same, wherever your code runs. Adding or upgrading a library can break your pipeline (e.g.

HOW TO INSTALL PYSPARK WITH PIP HOW TO

In this article, we’re going to show you how to start running PySpark applications inside of Docker containers, by going through a step-by-step tutorial with code examples ( see github).